[HTML][HTML] Radiomics and artificial intelligence in lung cancer screening
F Binczyk, W Prazuch, P Bozek… - Translational lung cancer …, 2021 - ncbi.nlm.nih.gov
Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76
million associated deaths reported in 2018. The key issue in the fight against this disease is …
million associated deaths reported in 2018. The key issue in the fight against this disease is …
A review on traditional machine learning and deep learning models for WBCs classification in blood smear images
In computer vision, traditional machine learning (TML) and deep learning (DL) methods
have significantly contributed to the advancements of medical image analysis (MIA) by …
have significantly contributed to the advancements of medical image analysis (MIA) by …
Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks
Deep learning techniques have been extensively used in computerized pulmonary nodule
analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in …
analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in …
A multi-scale 3D Otsu thresholding algorithm for medical image segmentation
Thresholding technique is one of the most imperative practices to accomplish image
segmentation. In this paper, a novel thresholding algorithm based on 3D Otsu and multi …
segmentation. In this paper, a novel thresholding algorithm based on 3D Otsu and multi …
Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset
Background Lung segmentation constitutes a critical procedure for any clinical-decision
supporting system aimed to improve the early diagnosis and treatment of lung diseases …
supporting system aimed to improve the early diagnosis and treatment of lung diseases …
Automated semantic lung segmentation in chest CT images using deep neural network
M Murugappan, AK Bourisly, NB Prakash… - Neural Computing and …, 2023 - Springer
Lung segmentation algorithms play a significant role in segmenting theinfected regions in
the lungs. This work aims to develop a computationally efficient and robust deep learning …
the lungs. This work aims to develop a computationally efficient and robust deep learning …
Lung nodule segmentation using salp shuffled shepherd optimization algorithm-based generative adversarial network
Lung nodule segmentation is an exciting area of research for the effective detection of lung
cancer. One of the significant challenges in detecting lung cancer is Accuracy, which is …
cancer. One of the significant challenges in detecting lung cancer is Accuracy, which is …
A weighted edge-based level set method based on multi-local statistical information for noisy image segmentation
C Liu, W Liu, W Xing - Journal of Visual Communication and Image …, 2019 - Elsevier
Image segmentation plays a fundamental role in image processing. Active contour models
have been widely used since they handle topological change easily and provide smooth …
have been widely used since they handle topological change easily and provide smooth …
[PDF][PDF] Improved version of graph-cut algorithm for CT images of lung cancer with clinical property condition
S Manoharan - Journal of Artificial Intelligence, 2020 - researchgate.net
In a clinical evaluation, the detection of lung cancer is a challenging task. Segmentation
methods are used to detect the extra growing nodule. Early diagnosis of lung cancer is …
methods are used to detect the extra growing nodule. Early diagnosis of lung cancer is …
Lung nodule detection based on ensemble of hand crafted and deep features
Lung cancer is considered as a deadliest disease worldwide due to which 1.76 million
deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer …
deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer …